TGFBR2‑dependent alterations of microRNA profiles in extracellular vesicles and parental colorectal cancer cells

  • Authors:
    • Fabia Fricke
    • Veronika Mussack
    • Dominik Buschmann
    • Ingrid Hausser
    • Michael W. Pfaffl
    • Jürgen Kopitz
    • Johannes Gebert
  • View Affiliations

  • Published online on: August 19, 2019
  • Pages: 925-937
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In colorectal cancer (CRC) with microsatellite instability (MSI), >90% of cases are affected by inactivating frameshift mutations of transforming growth factor β receptor type 2 (TGFBR2). TGFBR2 deficiency is considered to drive MSI tumor progression by abrogating downstream TGF‑β signaling. This pathway can alter the expression of coding and non‑coding RNAs, including microRNAs (miRNAs), which are also present in extracellular vesicles (EVs) as post‑transcriptional modulators of gene expression. In our previous study, it was shown that TGFBR2 deficiency alters the protein composition and function of EVs in MSI tumors. To investigate whether mutant TGFBR2 may also affect the miRNA cargo of EVs, the present study characterized miRNAs in EVs and their parental MSI tumor cells that differed only in TGFBR2 expression status. The HCT116‑TGFBR2 MSI cell line model enables the doxycycline (dox)‑inducible reconstituted expression of TGFBR2 in an isogenic background (‑dox, TGFBR2 deficient; +dox, TGFBR2 proficient). Small RNA sequencing of cellular and EV miRNAs showed that the majority of the miRNAs (263/471; 56%) were shared between MSI tumor cells and their EVs. Exploratory data analysis revealed the TGBFR2‑dependent cluster separation of miRNA profiles in EVs and MSI tumor cells. This segregation appeared to result from two subsets of miRNAs, the expression of which were regulated in a TGFBR2‑dependent manner (EVs: n=10; MSI cells: n=15). In the EV subset, 7/10 miRNAs were downregulated and 3/10 were upregulated by TGFBR2 deficiency. In the cellular subset, 13/15 miRNAs were downregulated and 2/15 miRNAs were upregulated in the TGFBR2‑deficient cells. The present study emphasizes the general overlap of miRNA profiles in MSI tumor cells and their EVs, but also highlights the impact of a single tumor driver mutation on the expression of individual miRNAs, as exemplified by the downregulation of miR‑381‑3p in TGFBR2‑deficient MSI tumor cells and their secreted EVs.


In colorectal cancer (CRC), a subset is characterized by the loss of DNA mismatch repair (MMR) function, leading to high frameshift mutation rates, predominately at repetitive DNA sequences (microsatellites). The resulting phenotype manifests as microsatellite instability (MSI) (1). A large number of genes are affected by microsatellite instability, however, only some of them are considered to be drivers of MSI cancer. One of the most frequent MSI tumor driver mutations occurs in a coding polyadenine (A10) tract in exon 3 of the transforming growth factor β receptor 2 (TGFBR2) gene (2,3). It is reported that >90% of MSI tumors are affected by frameshift mutations in the TGFBR2 gene, leading to impaired receptor expression and abrogated downstream signaling (3). The TGFBR2 protein is a transmembrane serine-threonine kinase and serves as the primary receptor for non canonical and canonical TGF-β signaling. In the canonical pathway, binding of the TGF-β1 ligand induces the dimerization of TGFBR2 and TGFBR1, leading to Smad-dependent intracellular signal propagation. Receptor associated Smad proteins, such as Smad2 and Smad3, are then phosphorylated, allowing complex formation with Smad4. The functional complex translocates into the nucleus, where it interacts with different transcription factors to control expression of several target genes and proteins that are crucial for maintenance of the colonic epithelium (4). As downstream mediators of TGFBR2 signaling, Smad proteins can also affect the processing and expression of non coding RNAs, including microRNAs (miRNAs) (5). With an average size of 22 nucleotides, miRNAs provide fine-tuning of protein abundance by silencing target gene expression at the post transcriptional level (6,7). Approximately 60% of protein coding genes are regulated by miRNAs, which therefore play a key role in maintaining cellular homeostasis (8).

As important regulators of numerous normal and patholog ical processes, including colorectal tumorigenesis, functional miRNAs, among other molecules, have been identified in the cargo of extracellular vesicles (EVs) (9-12). Following transfer to cells in the microenvironment or at more distant sites, the functional cargo of EVs can elicit biological responses in recipient cells (13). As the EV cargo composition reflects the type and molecular status of their cell of origin, EV miRNA levels are altered under different biological and pathological conditions. In our previous study, we showed that TGFBR2 deficiency alters the proteome of MSI CRC-derived EVs (14). However, whether the expression status of TGFBR2 can modulate the repertoire of miRNAs in MSI tumor-derived EVs remains to be elucidated. To bridge this gap of knowledge, the present study aimed to decipher the TGFBR2-dependent miRNA profile of MSI tumor cells and their secreted EVs. An established MSI cell line model (HCT116-TGFBR2) was used (15), which enables the analysis of TGFBR2-dependent alterations in an isogenic background (Fig. 1). Small RNA sequencing (RNA-Seq) identified shared and distinct miRNA signatures in EVs and their parental MSI tumor cells that are regulated in a TGFBR2-dependent manner.

Materials and methods

Cell culture

The human epithelial HCT116 MSI CRC cell line was obtained from the European Collection of Cell Cultures (Salisbury, UK). The generation of the HCT116-TGFBR2 and HCT116-Tet-On cell lines has been described previously (15,16). In both cell lines, the endogenous TGFBR2 gene is inactivated (TGFBR2 deficient, dT) by homozygous frameshift mutations in a polyadenine tract (A10) within exon 3. HCT116 Tet On cells are the parental cells of HCT116 TGFBR2. Both cell lines allow the constitutive expression of a doxycycline (dox) responsive transactivator (16). HCT116-TGFBR2 cells confer dox inducible expression of a TGFBR2 transgene (+dox, TGFBR2 proficient, pT), which was inserted as a single copy at a defined site in the HCT116-TGFBR2 genome (15). The HCT116-TGFBR2 and HCT116-Tet-On control cells were cultured in DMEM-F12 medium (Thermo Fisher Scientific, Inc., Waltham, MA, USA) supplemented with 10% FBS (Thermo Fisher Scientific, Inc.), 100 U/ml penicillin and 100 µg/ml streptomycin (Thermo Fisher Scientific, Inc.) in 5% CO2 atmosphere at 37°C. The Mycoplasma Detection kit (Minerva Biolabs, Berlin, Germany) was used, according to the manufacturer's protocol, to ensure that the cultured cells were free of mycoplasma contamination.

Extracellular vesicle isolation

Each of the four biological replicates was obtained from 5× T175 flasks containing 28×106 HCT116-TGFBR2 or HCT116-Tet-On cells. The cells were washed twice with PBS (Thermo Fisher Scientific, Inc.) and subsequently cultured in serum-free DMEM-F12 medium (17 ml/T175 flask). The HCT116-TGFBR2 cells were cultured in the presence of 0.5 µg/ml dox (+dox, pT; Sigma-Aldrich; Merck KGaA, Darmstadt, Germany) in order to induce the reconstituted expression of TGFBR2, or grown in the absence of dox (-dox, dT), which reflects the loss of TGFBR2 in MSI tumors. The ligand TGF β1 (10 ng/ml; Abcam, Cambridge, UK) was added to the dT and pT cells. In order to exclude any dox-related effects, two subsets of HCT116-Tet-On cells were also cultured in the presence or absence of dox. After 16 h, the medium was collected from both conditions, and the cells were harvested and stored at -80°C. The medium was subjected to sequential centrifugation and precipitation as reported previously (14). Briefly, the supernatants were cleared by differential centrifugation (480 × g, 5 min, 4°C; 2,000 × g, 10 min, 4°C), filtered (0.22 µm), and concentrated (40 fold) using Vivaspin-20 tubes (Sartorius, Göttingen, Germany). The EVs were precipitated from this concentrate using Total Exosome Isolation Reagent (Thermo Fisher Scientific, Inc.) and pelleted by centrifugation (20,000 × g, 1 h, 4°C). The EV pellets were lysed in QIAzol Lysis Reagent (Qiagen AB, Limburg, Netherlands) for RNA extraction, in RIPA buffer (50 mM Tris HCl pH 7.4, 150 mM NaCl, 1% Triton X-100, 1% Na deoxycholate, 0.1% SDS, 0.1 mM CaCl2 and 0.01 mM MgCl2) for protein extraction or resuspended in PBS for EV characterization.

Transmission electron microscopy (TEM)

The EV suspensions (5 µl) in PBS were left to settle onto 100 mesh formvar coated copper grids (Plano, Wetzlar, Germany), contrasted with 2% aqueous uranyl acetate (negative stain), air-dried and visualized using the JEM-1400 transmission microscope (JEOL, Ltd., Peabody, MA, USA) equipped with a Tietz 2k digital camera (TVIPS, Gauting, Germany) at 80 KV.

Nanoparticle tracking analysis

The size profiles and particle concentrations were assessed by nanoparticle tracking analysis (NTA) using the ZetaView PMX110 system with software 8.04.02 SP2 (Particle Metrix, Inning, Germany) according to the manufacturer's instructions. The EV suspensions were diluted 1:2,500 (v/v) in particle free PBS and analyzed at 11 positions. Data acquisition thresholds were set to a shutter of 100, a sensitivity of 95% and a frame rate of 30 frames/sec.

Protein extraction and Western blot analysis

The protein lysates of the EVs and cells were prepared in RIPA buffer supplemented with cOmplete Mini protease inhibitor cocktail inhibitor (Roche Diagnostics, Basel, Switzerland). Protein concentration was measured using the Bradford assay (Bio Rad Laboratories, Inc., Hercules, CA, USA). Per sample, 50 µg of protein was separated on 4-20% Bis-Tris gradient gels (Expedeon, Cambridge, UK) and blotted onto a nitrocellulose membrane (Thermo Fisher Scientific, Inc.). After blocking of the membranes in 5% milk/TBST, the following primary antibodies were used: mouse anti-CD63 (1:500, clone MX 49.129.5, cat. no. sc-5276, Santa Cruz Biotechnology, Inc., Dallas, TX, USA), mouse anti-CD9 (1:200, clone C4, cat. no. sc-13118, Santa Cruz Biotechnology, Inc.), mouse anti-β-actin (1:2,000, clone C4, cat. no. 0869100-CF, MP Biomedicals, Santa Ana, CA, USA), rabbit anti-Alix (1:1,000, clone EPR15314-33, cat. no. ab186429, Abcam), mouse anti-TSG101 (1:500, clone 4A10, cat. no. ab83, Abcam), rabbit anti-Syntenin (1:5,000, clone EPR8102, cat. no. ab133267, Abcam), goat anti-Calnexin (1:2,500, cat. no. WA AF1179a, Biomol, Hamburg, Germany), mouse anti-TGFBR2 (1:300, clone D2, cat. no. sc-17799, Santa Cruz Biotechnology, Inc.), rabbit anti-Smad2 (1:1,000, clone 86F7, cat. no. 3122S, Cell Signaling Technology, Inc., Danvers, MA, USA), and rabbit anti phosphorylated (p)Smad2 (1:1,000, Ser465/467, cat. no. 3101, Cell Signaling Technology, Inc.). The primary antibodies were diluted in 5% milk/TBST and incubated with membranes overnight at 4°C. The blots were then washed with TBST and incubated with a sheep anti mouse IgG HRP (1:5,000, cat. no. NXA931, GE Healthcare, Chicago, IL, USA), goat anti rabbit IgG HRP (1:2,500, cat. no. 7074, Promega Corporation, Madison, WI, USA) or donkey anti goat HRP (1:1,000, cat. no. sc 2020, Santa Cruz Biotechnology, Inc.) secondary antibody for 1 h at room temperature. The signals were detected using Western Lightning Plus ECL (Perkin Elmer, Inc., Waltham, MA, USA) and a ChemiDoc MP system (Bio Rad Laboratories, Inc.).

RNA extraction and quality control

Total RNA was extracted from the EVs and parental cells using the miRNeasy Mini kit (Qiagen GmbH). The cells and EVs were homogenized in 700 µl QIAzol Lysis Reagent (Qiagen GmbH) and total RNA was isolated according to the manufacturer's instructions. RNA was eluted in 30 µl RNase-free water. The yield and size distribution of the isolated RNAs were assessed by capillary electrophoresis using the RNA Nano and RNA 6000 Pico kit on the 2100 Bioanalyzer (Agilent Technologies GmbH, Waldbronn, Germany). Integrity values for cellular RNAs were calculated using the Bioanalyzer's RNA integrity number (RIN) algorithm. The RIN algorithm is designed to estimate RNA quality for eukaryotic cells but cannot be used for quality assurance of EV RNA due to lack of full-length 28S and 18S rRNA. For reverse transcription-quantitative polymerase chain reaction (RT-qPCR) analysis, the RNA concentrations were determined using a NanoDrop instrument (Thermo Fisher Scientific, Inc.).

Library preparation and small RNA sequencing

The total RNA eluates (30 µl) were vacuum-evaporated and resuspended in 8 µl nuclease free water. For small RNA library preparation, 6 µl of total RNA and the NEBNextMultiplex Small RNA Library Prep Set for Illumina (New England BioLabs, Ipswich, MA, USA) were utilized according to the manufacturer's instructions, including recommended adjustments for low RNA input. The PCR products were purified using the MinElute PCR Purification kit (Qiagen GmbH) prior to library evaluation using the DNA 1000 kit (Agilent Technologies GmbH) and the Bioanalyzer 2100. To allow size selection of miRNA-containing fragments at a length of 130-150 base pairs, the pooled library was separated on a high-resolution 4% agarose gel (MetaPhor Agarose, Lonza Rockland, Rockland, ME, USA) at 150 V at 4°C, and the corresponding bands were cut from the gel. Following gel extraction with the MinElute Gel Extraction kit (Qiagen GmbH), capillary gel electrophoresis was performed using the Bioanalyzer DNA High Sensitivity kit (Agilent Technologies GmbH) to control for the size and purity of the final library. Small RNA sequencing was conducted on a HiSeq 2500 using the HiSeq Rapid SBS Kit v2 (Illumina, Inc., San Diego, CA, USA) and 50 cycles of single-end sequencing-by-synthesis.

Data analysis

Raw data was imported into FastQC software (Babraham Bioinformatics, Cambridge, UK, version 0.11.7) to evaluate technical sequencing parameters including the per base sequence quality, indicated by the Phred quality score, and sequence length distribution (17). Adaptor sequences added to the 3'-end during library preparation were trimmed using Btrim (18). Reads with a length of <16 nucleotides ('Short') were excluded to avoid false positive mappings. Reads corresponding to other RNA classes [ribosomal ('rRNA'), small nuclear ('snRNA'), small nucleolar (snoRNA)' and transfer ('tRNA')] were identified by mapping to sequences obtained from RNAcentral, v9 (19), and omitted in subsequent analyses. The filtered reads were then aligned to human miRNA precursor sequences obtained from miRBase, release 22 (20), allowing for one mismatch using Bowtie (21). The unmapped reads were classified as 'Unmapped' and disregarded. The read count table generated by the sum of the hits per miRNA sequence was loaded into R (version 3.5.1; and used as input for the Bioconductor package DESeq2 (version 1.20.0; (22). Differential gene expression (DGE) analysis was conducted to evaluate the regulation of gene expression in a TGFBR2 dependent manner. Fold changes (dT EVs/pT EVs and dT cells/pT cells) were log2-transformed for statistical data analysis. The obtained P values were adjusted for multiple comparisons according to Benjamini and Hochberg (23). The baseMean of each miRNA, which reflects the mean expression across all samples, was set to ≥20 reads. An adjusted P-value (p-adj) of P≤0.05 and a log2 fold change (FC)≥|0.585| were applied as cut off values. For further calculation and visualization of the DESeq2 results, the following packages were used: data.table, RColorBrewer, gplots, ggplot2, genefilter, ggfortify, VennDiagram, dendextend, reshape2, and scales. Outliers were detected using Cook's distance test (22). Exploratory data analysis was performed by displaying the regularized logarithm transformed data in principal component analysis (PCA), heatmap analysis, and hierarchical clustering by Euclidean distances. Interaction network analysis was performed using miRNet ( (24) based on conventional hypergeometric tests and the Reactome pathway database ( (25).

RT-qPCR analysis

Total RNA (100 ng) was subjected to RT reactions using the miRCURY LNA RT kit (Qiagen GmbH) according to the manufacturer's protocol. UniSp6 was used as a spike-in control. For real-time PCR analysis, the cDNA products were diluted 1:10 (v/v) in nuclease-free water. qPCR was performed in a total volume of 10 µl using the miRCURY LNA SYBR Green PCR kit (Qiagen GmbH). The following miRCURY LNA miRNA PCR Assay primers (Qiagen GmbH) were used: hsa-miR-376a-3p (cat. no. YP00204508), hsa-miR-381-3p (cat. no. YP00205887), hsa-miR-379-5p (cat. no. YP00205658), hsa-miR-181a-2-3p (cat. no. YP00204142), hsa miR 30d 5p (cat. no. YP00206047), hsa miR 362 3p (cat. no. YP00205612), hsa miR-92b-3p (cat. no. YP00204384), hsa-miR-25-3p (cat. no. YP00204361) and hsa miR-744-5p (cat. no. YP00204663). Nuclease free water was used as a no template negative control. Each PCR was performed in triplicate on a StepOnePlus Real-Time PCR system (Thermo Fisher Scientific, Inc.) at 95°C for 2 min, followed by 40 cycles of 95°C for 10 sec and 56°C for 1 min. The specificity of amplification was assessed by melting curve analysis. Raw quantification cycle (Cq) values were generated and data were analyzed using StepOnePlus software (version 2.1; Thermo Fisher Scientific, Inc.). The baseline was set automatically and the threshold values were adjusted manually. Relative quantification was conducted using the 2−∆∆Cq method (26,27). Reference candidates stably expressed across all replicates of EVs and parental MSI cells were identified from next generation sequencing data using NormFinder ( (28) and GeNorm ( (29). miR-30d-5p, miR-362-3p, miR-744-5p, and miR-92b-3p and miR-25-3p were used as endogenous controls for normalizing target miRNA expression levels in EVs and MSI cells, respectively. Five significantly regulated miRNAs were selected for validation (cells: miR-381-3p, miR-379-5p and miR-181a-2-3p; EVs: miR-381-3p and miR-376a-3p). Statistical significance (P≤0.05) was assessed using Student's t-test.

Statistical analysis

Statistical calculations were performed in SigmaPlot (version 11.0; Systat Software, San Jose, CA, USA), R software (version 3.5.1) and Prism (version 7.00; GraphPad Software, Inc., La Jolla, CA, USA). The quantitative miRNA expression levels identified by RNA-Seq were compared between the dT and pT samples using the Bioconductor Package DESeq2 (version 1.20.0). Fold changes (dT EVs/pT EVs and dT cells/pT cells) were log2 transformed for statistical data analysis and the obtained P values were adjusted for multiple comparisons according to Benjamini and Hochberg (23). A p-adj of P≤0.05 and a log2FC≥|0.585| were applied as cut-off values. For the qPCR data, statistical significance between the quantitative miRNA expression levels was assessed using an unpaired, two-tailed Student's t-test. P≤0.05 was considered to indicate a statistically significant difference. Data in the text and in tables are presented as the mean ± standard deviation.


General extracellular vesicle characteristics are not altered by the cellular expression of TGFBR2

In order to investigate TGFBR2-dependent alterations in MSI tumor cells and their secreted EVs, the well-characterized HCT116-TGFBR2 cell line was used as a model system (15). In the absence of dox, HCT116-TGFBR2 cells are TGFBR2 deficient (-dox, dT), which reflects the condition of most primary MSI tumors that have lost receptor expression during tumor progression. By contrast, in the presence of dox, these cells show reconstituted receptor expression (+dox, pT), which allows the identification of TGFBR2-dependent alterations in an isogenic background. Prior to EV preparation, reconstitution of a functional TGF-β pathway was examined by Western blot analysis (Fig. S1). In the absence of dox and in the presence of the TGF-β1 ligand, the HCT116-TGFBR2 cells lacked TGFBR2 expression and pSmad2 was almost undetectable. However, in the presence of dox and the ligand, these cells showed reconstituted expression of TGFBR2 and downstream signaling, as demonstrated by the activation of pSmad2. Following establishment of the induction control, the EVs were isolated from the pT cells and dT cells, and their morphology, size, concentration and EV marker protein expression were assessed by different methods (Fig. 1) in compliance with the MISEV2018 recommendations (30). First, using TEM, single EVs appeared as round, cup shaped, lipid bilayer-enclosed structures within a size range of 30-150 nm in all preparations (Fig. 1A). Second, NTA analysis independently confirmed the observed size distribution of 127.6 nm (mean 139.5±52.3 nm) for dT EVs and 124.5 nm (mean 140.6±52.4 nm) for pT EVs (Fig. 1B). The initial particle concentrations were calculated, resulting in 4.6x104 (dT EVs) and 4.5×104 (pT EVs) particles per ml cell culture supernatant. Third, Western blot analysis revealed the expression of EV specific and cell specific marker proteins (Fig. 1C). Tetraspanins CD63 and CD9 were expressed in lysates of the EVs but not in lysates of the parental cells. The EV marker protein Syntenin was also found to be exclusively detected in EV lysates. TSG101 and Alix were enriched in the protein lysates of EVs compared with those of their parental cells. The endoplasmic reticulum protein Calnexin and cytoskeletal β actin served as controls. Both were detected in the cell lysates but not in the EV lysates, excluding any contamination of EV lysates with cellular debris. These results demonstrate the successful enrichment of a comprehensively characterized population of EVs from dT cells and pT cells, and showed the reproducibility of EV isolation, thus enabling subsequent analysis of miRNA profiles in a TGFBR2-dependent manner.

Technical assessment reveals high RNA quality and sequencing accuracy

Subsequently, total RNA was isolated from four biological replicates of dT cells and pT cells and from the dT EVs and pT EVs derived thereof. For comparative RNA-Seq analysis, high quality RNA of sufficient quantity is of utmost importance. When total cellular RNA was analyzed by capillary electrophoresis, similar levels of RNA were detected in pT cells (1.9±1.0 µg) and dT cells (2.3±1.2 µg). Subsequent examination of the EVs shed by these cells revealed lower and marginally different yields of RNA from pT EVs (344.8±129.6 ng) and dT EVs (254.4±107.2 ng) (Table I). Further control of RNA integrity resulted in a mean RINs of 9.98±0.05 (pT cells) and 9.85±0.13 (dT cells), indicating an RNA quality of high grade (Table I). Small RNA was then profiled by RNA-Seq. Based on this analysis, all samples had a Phred score ≥38 (mean 39.04), reflecting a high sequencing accuracy (31). The length distribution of the sequencing data exposed a mono peak at 22 nucleotides in all groups (Fig. S2). Libraries of 5.56×106±6.40×105 (pT cells), 5.47×106±6.87×105 (dT cells), 6.95×106±1.28×106 (pT EVs) and 6.10×106±5.11×105 (dT EVs) reads were generated (Table I). Although sequencing of RNA from EVs resulted in higher read counts compared with that of cellular RNA, the TGFBR2 expression status did not significantly alter the library size (Table I; Fig. S2).

Table I

Technical assessment of small RNA sequencing.

Table I

Technical assessment of small RNA sequencing.

Technical parameterParental MSI cells
pT cellsdT cellspT EVsdT EVs
RNA (ng) ± SD 1,914.4±1,000.62,304.8± 1,202.4344.8±129.6254.4±107.2
RIN ± SD9.98 ±0.059.85±0.13NANA
Libraries (reads) ± SD 5.56×106±6.40×105 5.47×106±6.87×105 6.95×106±1.28×106 6.10×106±5.11×105
miRNA reads (%)63646163
Total miRNAs (reads) ± SD 3.5×106±3.6×105 3.5×106±4.8×105 4.2×106±8.4×105 3.8×106±4.8×105
Numbers of miRNAs360357380367

[i] Presented numbers are mean values calculated from four biological replicates. TGFBR2, transforming growth factor β receptor type 2; dT, TGFBR2 deficient; pT, TGFBR2 proficient; EVs, extracellular vesicles; RIN, RNA integrity number; miRNA, microRNA; NA, not applicable.

Subsequently, the reads were mapped to distinct classes of small non coding RNAs (Fig. 2). Although the cells showed a higher amount of snoRNA, the mapping distribution indicated an enrichment of tRNA and snRNA in the EVs. Reads mapping to miRNAs accounted for the major proportion (60.6 63.9%) of all reads in all four groups (Fig. 2, Table I). In particular, 3.5×106±3.6×105 (pT cells), 3.5×106±4.8×105 (dT cells), 4.2×106±8.4×105 (pT EVs) and 3.8×106±4.8×105 (dT EVs) reads mapped to miRNAs (Table I). Following application of an expression threshold of at least 20 reads per replicate for each miRNA, similar numbers of individual miRNAs were identified in all four groups (Table I). Taken together, these analyses indicated a high RNA quality and sequencing accuracy, allowing further investigation of TGFBR2-dependent miRNA profiles in EVs and parental MSI tumor cells.

Clustering of miRNA profiles is determined by the expression of TGFBR2

A total of 471 distinct miRNAs were identified among all four groups at a qualitative expression level. Venn diagram comparison showed the presence of individual and shared miRNA subsets among these groups (Fig. 3). The majority (263/471; 56%) of the identified miRNAs were shared between the EVs and parental MSI tumor cells.

In addition to this overlap, 100 miRNAs were exclusively detected in the EVs, of which 82 candidates were identified in both dT EVs and pT EVs. Several miRNAs were exclusively expressed in the dT EVs (n=4; miR-122-5p, miR 92a 1 5p, miR-1343-3p and miR 146b 3p) or pT EVs (n=14; miR-20b-5p, miR-616-5p, miR-19b-1-5p, miR-561-3p, miR-539-3p, miR-382-3p, miR-6516-3p, miR-3136-5p, miR-96-3p, miR-3140-3p, miR-1249-3p, miR-106a-5p, miR-548ai/-570-5p and miR-3065-5p).

By contrast, 81 miRNAs were exclusively detected in the MSI tumor cells, of which 71 miRNAs were detected in both dT and pT cells. TGFBR2-dependent unique expression clusters were also identified in the dT cells (n=4; miR-4429, miR-2355-3p, miR-320e and miR-371a-5p) and pT cells (n=6; miR-505-5p, miR-411-5p, miR-223-3p, miR-5001-5p, miR-184 and miR 495 3p).

One candidate, miR-200c-5p, was identified in both the dT EVs and dT cells but was not detected in samples derived from the TGFBR2-proficient condition. By contrast, two candidates (miR-323a-3p and miR-382-5p) were only identified in the pT EVs and parental pT cells.

Based on these results, exploratory data analysis was performed to assess the clustering of individual samples. Hierarchical cluster analysis (HCA) and principal component analysis (PCA) identified one of the four biological replicates in the EV group as an outlier. The altered miRNA expression pattern of this outlier (EV_V1) was also indicated by Cook's distance test (22) and was thus excluded from further analysis. As shown in Fig. 4A, PCA clearly separated all biological replicates of the EVs from the parental MSI cells along the first principal component (PC1) with a variance of 81.04%. This clustering indicated substantial differences in the expression of miRNAs between the EVs and parental cells. The second principle component (PC2) accounted for 2.68% of the overall variance. The distances of miRNA expression were higher between the EVs and parental cells than between TGFBR2 conditions within one sample category. In addition to PCA, miRNA expression was analyzed by HCA (Fig. 4B). The HCA dendrogram showed a distinct fractionation of cells and EVs in addition to visible cluster distinction depending on the TGFBR2 expression status. The heatmap illustrated clear cluster changes in miRNAs between EVs and their parental cells (Fig. 4B). Considering the grouping performance of PCA and HCA, the miRNA profiles of the EVs and the cells differed in a TGFBR2 dependent manner.

TGFBR2 deficiency modulates miRNA profiles in EVs and MSI tumor cells

In the subsequent step, differential gene expression analyses were performed to quantify miRNA candidates with significant TGFBR2 mediated expression differences. Sequencing data were subjected to DESeq2 analysis in order to compare miRNA expression between the dT and pT conditions in EVs and parental cells separately. Fold changes (dT/pT) were calculated to quantify the differential expression patterns. Applying stringent filtering criteria (log2FC≥|0.585|, adj. P≤0.05, mean expression ≥20 reads), DESeq2 analysis revealed TGFBR2-regulated miRNAs in HCT116 TGFBR2 cells and their EVs (Fig. 5).

In the EVs, the expression of 10 miRNAs was regu lated in a TGFBR2 dependent manner. Of the candidates, 7/10 (miR-376a-3p, miR-381-3p, miR-410-3p, miR-376c, miR-889-3p, miR-323a-3p and miR-376b-3p) were downregu lated in the dT EVs, the remaining 3/10 miRNAs (miR 320d, miR-320c and miR-3187-3p) were upregulated in the dT EVs, as indicated by positive log2FC (Fig. 5A, Table II). In silico network analysis predicted interactions between these 10 TGFBR2 regulated miRNAs and 1,022 target genes (Fig. S3) (24). Subsequent functional enrichment analysis revealed that signaling by the TGF β receptor complex was the most affected (P=0.0002) pathway (Table SI). More specifically, among all identified target genes, 18 candidates appeared to mediate signal transduction downstream of TGFBR2.

Table II

TGFBR2 regulated candidates in EVs and their parental MSI tumor cells.

Table II

TGFBR2 regulated candidates in EVs and their parental MSI tumor cells.

miRNAlog2FCp-adjMean expression
EVs (n=10)
 miR-376a-3p−1.18 2.46×105971.86
 miR 320d0.960.00593,474.59
Parental MSI cells (n=15)
 miR-134-5p−1.93 5.81×10520.39
 miR-543−1.87 2.54×101775.29
 miR 323a 3p−1.700.000520.47
 miR-381-3p−1.62 1.23×1053548.64
 miR-411-5p−1.59 8.95×10633.38
 miR 409 3p−1.57 5.33×1022237.54
 miR-382-5p−1.56 3.55×10532.84
 miR 379 5p−1.50 1.43×10-18149.75
 miR 654 3p−1.32 4.56×10537.48
 miR 495 3p−1.290.002435.67
 miR 372 3p−0.730.029952.31
 miR-181a-2-3p−0.70 8.21×1012506.83
 miR 31 3p0.630.0019140.31

[i] Mean expression of ≥20 reads/miRNA, adj. P≤0.05, log2FC≥|0.585| were applied as cut off values. Data in each column (log2FC, p adj and mean expression) represent the mean values calculated from three (EVs) or four (cells) biological replicates. All values were determined using the DESeq2 algorithm. Negative log2FC values indicate downregulation by loss of TGFBR2, whereas positive log2FC values indicate upregulation by lack of TGFBR2. miR, microRNA; TGFBR2, transforming growth factor-β receptor type 2; EVs, extracellular vesicles; MSI, microsatellite instability; p-adj, adjusted P-value; FC, fold change.

When miRNA abundance in parental MSI cells was examined, 15 miRNAs were regulated in a TGFBR2 dependent manner (Fig. 5B). In particular, 2/15 miRNAs (miR 31 3p and miR-4521) were upregulated and 13/15 miRNAs (miR-381-3p, miR-409-3p, miR-379-5p, miR-543, miR-181a-2-3p, miR-411-5p, miR-382-5p, miR-654-3p, miR-134-5p, miR-323a-3p, miR-495-3p, miR-889-3p and miR-372-3p) were downregulated in dT cells (Table II). Apart from these differences, three TGFBR2-dependent miRNAs (miR-381-3p, miR-323a-3p and miR-889-3p) appeared to be downregulated in both dT EVs (3/10) and dT cells (3/15) (Fig. 5).

Taken together, the tumor driver mutation affecting TGFBR2 impacts the expression of 25 miRNAs (EV n=10; cells n=15). From these 25 candidates, three candidates were downregulated in both dT cells and their dT EVs, whereas the majority of TGFBR2 dependent miRNAs (19/25) were specifically regulated in MSI cells and their secreted EVs.

Differential miRNA expression is mediated by reconstituted expression of TGFBR2 rather than dox treatment

To validate the TGFBR2-regulated miRNAs in the MSI derived EVs, the expression levels of miR-381-3p and miR-376a-3p were examined by RT-qPCR analysis (Fig. 6A, Table III). With a FC of 0.55, miR-381-3p exhibited significantly (P=0.006) lower expression in the dT EVs. Similarly, miR 376a 3p exhibited significantly (FC 0.59; P=0.013) lower expression levels in the dT EV samples compared with those in the pT EV samples, thereby confirming the results of the small RNA-Seq analysis.

Table III

TGFBR2 regulated gene expression and the effect of doxycycline.

Table III

TGFBR2 regulated gene expression and the effect of doxycycline.

Control (FC) RT-qPCR
Parental MSI cells

[i] FC values (TGFBR2 deficient/TGFBR2 proficient) represent mean values calculated from four biological replicates. Control values were obtained from HCT116-Tet-On cells. miR, microRNA; FC, fold change; TGFBR2, transforming growth factor-β receptor type 2; MSI, microsatellite instability; EVs, extracellular vesicles; NGS, next-generation sequencing; RT-qPCR, reverse transcription-quantitative polymerase chain reaction.

For validation of the TGFBR2 regulated miRNAs in the parental MSI HCT116 TGFBR2 cells (Fig. 6B, Table III), the expression levels of miR-381-3p, miR-379-5p and miR-181-2-3p were investigated. A statistically significant (FC 0.39; P=0.005) downregulation of miR-381-3p was found in the dT cells compared with that in the pT cells. For the two cellular candidates, miR-379-5p (FC 0.39; P=0.154) and miR-181a-2-3p (FC 0.80; P=0.745), a trend towards lower expression in dT cells was observed, although this was not statistically significant (Fig. 6B).

In order to investigate potential changes of miRNA profiles caused by dox treatment, EVs were isolated from the +dox and dox treated HCT116 Tet On control cells. Differential miRNA expression was not observed in these control cells or their derived control EVs (Table III), thus excluding any dox-related effect on the identified and successfully validated TGFBR2-dependent miRNA expression signatures in the EVs and parental MSI tumor cells.


The majority of cases of MSI CRC are affected by the mutational inactivation of TGFBR2, allowing MSI tumor cells to evade normal TGF β signaling and gain oncogenic growth advantages. In an attempt to dissect the inherent complexity of molecular and biological mechanisms associated with TGFBR2 signaling in normal and tumor cells, the present study established a genetically modified MSI CRC cell line, referred to as HCT116 TGFBR2. This MSI model cell line allows inducible, reconstituted expression of a wild type TGFBR2 transgene and controlled activation of canonical TGF β signal transduction in an isogenic background (15). As a consequence, the downstream effects and modifications identified in the EV cargo can be assigned specifically to the TGFBR2 expression status of MSI cells. For example, our previous studies showed that the reconstituted expression of TGFBR2 alters the glycophenotype of MSI tumor cells (15,32,33), and tumor driver induced glycome changes have been linked to TGFBR2 regulated genes (34). Additionally, the reconstituted expression of TGFBR2 can cause differential de novo protein expression (35). In addition to these (glyco)proteomic changes of MSI cells, the reconstituted expression of TGFBR2 can modulate the protein cargo of MSI derived small EVs (14). Although TGFBR2 serves a crucial role in the MSI pathway of CRC development and progression, knowledge of the TGFBR2 dependent effects on the molecular cargo of small EVs released by MSI tumor cells remains limited. The present study provides evidence for TGFBR2 regulated miRNA expression profiles in EVs and corresponding parental cells that exhibit the MSI phenotype. Although gene expression differences between CRC and normal tissues and CRC cells and their EVs have been identified (36,37), the contribution of TGFBR2 to the expression patterns of non-coding RNA in MSI tumor cells and shed vesicles has not been investigated previously. Using high-throughput small RNA-Seq and the well characterized HCT116 TGFBR2 model system, the present study identified 25 candidate miRNAs (cells n=15; EVs n=10) for which differential expression appears to be regulated in a TGFBR2-dependent manner. As TGFBR2 deficiency is a hallmark of the majority of primary MSI tumors and is considered to be a MSI driver, those miRNAs with altered expression upon TGFBR2 deficiency were of interest. In total, 56% of the miRNAs were found to be expressed in all four groups. At a qualitative level, this finding emphasizes the general overlap of miRNA profiles between MSI CRC cells and their secreted EVs but also indicated TGFBR2 dependent differences among these groups. More refined and quantitative analysis by DESeq2 then provided additional evidence for the impact of a single tumor driver mutation on altering cellular and EV miRNA profiles. In particular, TGFBR2 deficiency caused the upregulation and downregulation of five and 20 miRNAs, respectively. In independent RT-qPCR experiments, the differential expression of four TGFBR2 regulated miRNAs (miR-381-3p, miR-376a-3p, miR-379-5p and miR-181a-2-3p) was successfully validated. One of the validated candidates, miR-381-3p was downregulated in both dT EVs and dT cells. Accordingly, it was predicted that TGFBR2 deficiency leads to the downregulation of miR-381-3p in vivo. Such altered expression levels are expected to be of functional relevance and may contribute to MSI-specific tumor characteristics.

Several studies have shown that miR-381-3p can play a dual role in carcinogenesis. Depending on the cancer entity, it can confer tumor promoting or suppressive effects (38,39). For CRC, its expression was reported to be significantly downregulated in primary tumor tissue compared with that in the normal colon mucosa (36,40). As reported by Hu et al, miR-381-3p is the only miRNA that targets the 3'-untranslated region of both Twist and Snail mRNA in the colon epithelium (41). TGFBR2-mediated TGF-β signaling can trigger the expression of Twist and Snail and thereby facilitate the conversion of epithelial cells into motile mesenchymal cells (42,43). As miR-381-3p has an inhibitory effect on epithelial-to-mesenchymal transition (EMT) through targeting Twist and Snail in colon cells, it is predicted that MSI tumors may gain invasive properties by downregulating miR-381-3p. In agreement with this hypothesis, MSI tumors typically invade the local tumor microenvironment (TME), have a lower propensity to metastasize to distant tissues and have a more favorable prognosis compared with microsatellite stable (MSS) phenotypes (44). Furthermore, liver metastases and poor survival rates of patients with CRC have been linked to elevated expression levels of miR-181a-2-3p (45,46), which is a direct target of the TGF-β pathway (47). This is in line with the data obtained in the present study demonstrating elevated expression levels of miR-181a-2-3p in pT cells and decreased expression levels in dT cells. Functional studies have shown that the restoration of miR-381-3p hindered the migratory and invasive capacity and proliferation of CRC cells by directly targeting LRH 1 and Twist 1 (36,40). Twist 1 can also be repressed by miR 134 5p (48), another candidate downregulated in dT cells. It was described that the overexpression of miR-134-5p resulted in repression of the proliferation and growth of CRC cells (49). Our previous study reported that the reconstitution of TGFBR2 in HCT116-TGFBR2 cells significantly reduced cellular proliferation (50). Based on the evidence mentioned above, this decreased proliferation rate in pT cells may at least partly be caused by elevated expression levels of miR-381-3p and miR-134-5p (36,40,49). However, the possibility that multiple factors account for the altered proliferation rate cannot be excluded.

In gastric cancer, miR-381-3p and its target TMEM16A has been shown to contribute to cell invasion by promoting TGF-β secretion (51,52). TGF-β is a potent inducer of EMT and thus serves a crucial role in cancer invasion, motility and metastasis (53). miR-381-3p suppressed TGF-β signaling and EMT moderately by targeting TMEM16A. In this context, TGF β may operate as a mediator between miR-381-3p, TMEM16A and EMT (52). The process of EMT requires the tight control of cell junctions. It was reported that the tight junction proteins occludin and zonula occludens (ZO-1) are significantly higher expressed when miR-381-3p is downregulated in intestinal epithelial cells (54). This is in agreement with our previous proteomic study, which showed that ZO-1 was exclusively expressed in dT EVs, which exhibited significantly lower expression levels of miR-381-3p (14). This correlation suggests a functional role of miR-381-3p in EVs, which may contribute to the modulation of the TME by altering tight junctions at local and more distant sites. Although emerging evidence described the expression and functional relevance of miR-381-3p in various tissues, its expression and biological role in secreted EVs remains to be fully elucidated. Among other miRNA candidates, increased levels of miR-381-3p have been detected in small EVs isolated from TGF β1 stimlutated A549 human lung cancer cells with a mesenchymal phenotype, compared with small EVs isolated from untreated epithelial A549 cells or 16HBE human normal bronchial epithelial cells (55). In a previous study focusing on CRC, EVs were isolated from a cohort of 100 patients with CRC (n=25 each of stages I-IV) and 50 healthy controls, and the EV RNA content was compared between the two groups (56). Based on RNA-Seq data, it was reported that the number of CRC associated miRNA isoforms (isomiRs) detected in EVs isolated from patients with CRC increased significantly with disease progression. Additionally, isoforms of miR-381-3p were identified in the cargo of EVs with significant differences between disease stages (56). In a follow up study, miR-381-3p and two other TGFBR2 regulated candidates (miR-376a-3p and miR-320d) were found to be downregulated in EVs isolated from patients with stage I and II CRC compared with the expression levels detected in EVs from healthy donors (57). In the present study, the downregulation of miR-381-3p and miR-376a-3p was detected in dT EVs, whereas higher expression levels of miR-320d were detected in dT EVs. Similarly, elevated expression levels of miR-320c were observed in dT EVs. Wang et al reported that miR-320c was significantly upregulated in plasma EVs from patients with early stage CRC (57). However, they did not include information regarding the MSS and MSI status of the CRC patient cohort, making it impossible to connect the observed changes in expression to the MSI phenotype or lack of TGFBR2 expression. In addition to miR-320c and miR-320d, the present study detected increased expression levels of miR-3187-3p in dT EVs. Experimental evidence suggests that miR-3178-3p is specifically sorted into CRC-derived EVs, leading to elevated levels compared with cellular miR-3178-3p expression (37). Further evidence has shown that increased expression levels of miR-3178-3p in the colonic epithelium can be caused by single nucleotide polymorphisms affecting the TGFBR1 gene (58). In the present study, an association between miR-3178-3p and TGFBR2 expression status was found. However, the function of miR-3178-3p in the pathogenesis of CRC remains to be elucidated.

By comparing miRNA expression levels between dT cells and pT cells, two miRNAs (miR-4521 and miR-31-3p) were upregulated in dT cells. It has been reported that miR-4521 is significantly upregulated in colon cancer stem cells compared with that in non stem cells (59). Of note, TGF β signaling has emerged as key pathway for controlling stem cell renewal and the differentiation of intestinal epithelial cells (60). miR-4521-3p was also upregulated in patients with inflamed colonic epithelium of colitis ulcerosa (61). MSI colorectal tumors are highly immunogenic tumors and are often characterized by marked lymphocyte infiltration and inflammatory reactions (44). Therefore, it was hypothesized that high levels of miR-4521-3p may favor the inflammatory environment often observed in MSI tumors. The second candidate found to be upregulated in dT cells, miR-31-3p, has also been associated with intestinal inflammation (62). Olaru et al suggested that the expression of miR-31-3p increases during the progression of inflammation-associated intestinal neoplasia (63). Experiments have shown that miR-31-3p can repress the transcription factor E2F2 that controls the expression of several TGF β target genes, including survivin, c myc and cyclin A2 (64). Using an miRNA array, Nosho et al reported higher expression levels of miR-31-3p/-5p in BRAF (V600E) mutated CRC compared with BRAF wild-type CRC (65). It was suggested that high expression levels of miR-31 are associated with the MSI phenotype, although they did not investigate the expression status of TGFBR2. In the present study, it was shown for the first time, to the best of our knowledge, that the upregulation of miR-31-3p may positively correlate with TGFBR2 deficiency in MSI tumor cells. In addition to being a potential MSI marker, other studies have highlighted the potential of miR-31-3p as a predictive biomarker of anti-epidermal growth factor receptor therapy efficacy for patients with RAS wild type and metastatic CRC (66).

In conclusion, the present study linked the regulation of 25 miRNAs in EVs and their MSI tumor cells to the expression status of TGFBR2. Although the study did not investigate which intracellular pathways downstream of TGFBR2 may affect the differential miRNA expression, specific tumor driver-dependent alterations in miRNA expression were identified in EVs and parental MSI tumor cells by combining a well-defined dox-inducible model system, a reproducible EV isolation protocol and high-throughput small RNA-Seq. In general, the results emphasize the broad overlap of miRNA content in EVs and their secreting cells. In particular, this study highlights the impact of the recurrent MSI tumor driver mutation affecting TGFBR2 on altering the miRNA signatures of EVs and their parental MSI CRC cells.

Future investigations are required to determine which intracellular pathways downstream of TGFBR2 caused the observed differential miRNA expression patterns. Although the TGF-/dox-mediated activation of canonical signaling was shown in the model system, ligand binding can also stimulate several non-canonical pathways downstream of the receptor that may have an impact on miRNA regulation. This can be addressed in further experiments by selectively targeting each of these different pathways using specific inhibitors. In addition, the results of the present study require confirma tion in additional MSI CRC cell lines and the role of miRNA candidates in MSI tumorigenesis requires assessment though functional studies.

Supplementary Data



adjusted P-value


colorectal cancer


quantification cycle


differential gene expression






TGFBR2 deficient


epithelial-to-mesenchymal transition


extracellular vesicle


fluorescent unit


hierarchical cluster analysis


log2 fold change




microsatellite instability


microsatellite stability


nanoparticle tracking analysis


principal component analysis


TGFBR2 proficient


ribosomal RNA


reverse transcription-quantitative polymerase chain reaction


RNA sequencing


small nucleolar RNA


small nuclear RNA


transmission electron microcopy


transforming growth factor-β receptor type 2


tumor microenvironment


transfer RNA


The authors gratefully acknowledge Mrs. Ulrike Ganserer (EM Lab, University Hospital Heidelberg), Mrs. Sigrun Himmelsbach and Mrs. Vera Fuchs (Dept. of Applied Tumor Biology, University Hospital Heidelberg) for their tech nical assistance, Mr. Benedikt Kirchner (Dept. of Animal Physiology and Immunology, Technical University of Munich) and Dr Christine Wurmser (Animal Breeding, Technical University of Munich) for their support in sequencing and processing small RNA libraries, and Claussen Simon Stiftung for their encouragement.


This study was supported by intramural funding from the Technical University of Munich (MP) and the University Hospital Heidelberg (grant nos. F.204078 to JG and F.204008 to JK).

Availability of data and materials

Sequencing data were deposited in the European Nucleotide Archive (ENA) under accession no. PRJEB30305 ( (67). EV data were submitted to the EV TRACK knowledgebase (; EV track ID: EV180072) (68).

Authors' contributions

JG, JK, MP, FF, VM and DB designed the experiments. FF, VM, IH performed the experiments. All authors contributed to data analysis. JG, JK, MP, FF, VM and DB prepared the manuscript. All authors have approved the final manuscript.

Ethics approval and consent to participate

Not applicable.

Patient consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.



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Volume 55 Issue 4

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Fricke F, Mussack V, Buschmann D, Hausser I, Pfaffl MW, Kopitz J and Gebert J: TGFBR2‑dependent alterations of microRNA profiles in extracellular vesicles and parental colorectal cancer cells. Int J Oncol 55: 925-937, 2019
Fricke, F., Mussack, V., Buschmann, D., Hausser, I., Pfaffl, M.W., Kopitz, J., & Gebert, J. (2019). TGFBR2‑dependent alterations of microRNA profiles in extracellular vesicles and parental colorectal cancer cells. International Journal of Oncology, 55, 925-937.
Fricke, F., Mussack, V., Buschmann, D., Hausser, I., Pfaffl, M. W., Kopitz, J., Gebert, J."TGFBR2‑dependent alterations of microRNA profiles in extracellular vesicles and parental colorectal cancer cells". International Journal of Oncology 55.4 (2019): 925-937.
Fricke, F., Mussack, V., Buschmann, D., Hausser, I., Pfaffl, M. W., Kopitz, J., Gebert, J."TGFBR2‑dependent alterations of microRNA profiles in extracellular vesicles and parental colorectal cancer cells". International Journal of Oncology 55, no. 4 (2019): 925-937.